System and method for providing targeted recommendations to segments of users of a virtual space

ABSTRACT

Targeted recommendations may be provided to specific user segments. The users may be segmented on or more user parameters that facilitate targeted provision of recommendations to the individual segments of users. A recommendation may prompt a user to take a recommended action in the game.

FIELD

The disclosure relates to providing targeted recommendations to segmentsof users of a virtual space.

BACKGROUND

The provision of recommendations to users of a virtual space is known.Known systems implement various rudimentary features to facilitateproviding recommendations to a user regarding his or her experience in avirtual space. Conventional systems leave users of virtual spaces in anunsatisfied or discouraged state when those users are faced withsituations in the virtual space that may decrease their enjoyment of thevirtual space.

SUMMARY

One aspect of the disclosure relates to providing recommendations tosegments of users of a virtual space. The users may be segmented on ormore user parameters that facilitate targeted provision ofrecommendations to the individual segments of users. A targetedrecommendation may enhance a user's enjoyment of the virtual spaceand/or increase the chances of success for the user in the virtualspace.

A system configured to provide providing recommendations to segments ofusers of a virtual space may include a server that operates in aclient/server architecture with one or more client computing platformsused by the users to access the virtual space. The server may beconfigured to execute one or more of a space module, a parameter module,a segmentation module, a recommendation generation module, arecommendation transmission module, a recommendation tracking module, arecommendation analysis module, and/or other modules.

The space module may be configured to execute an instance of a virtualspace. The space module may be configured to implement the instance ofthe virtual space to facilitate participation by users in a game withinthe virtual space by determining view information from the instance andtransmitting the view information to the client computing platformsassociated with the users. The view information may facilitate thepresentation of views of the virtual space to the users by the clientcomputing platforms.

The parameter module may be configured to obtain values of userparameters for the individual users. The user parameters may include oneor more of a demographic parameter, a social parameter, a gameparameter, an activity parameter, and/or other parameters. A demographicparameter may include one or more of age, sex, geographic location,language, income, education, career, marital status, and/or otherdemographic parameters. A social parameter may include one or more of aparameter derived from a social graph in a social network service, anin-game relationship, a platform from which the virtual space isaccessed, and/or other social parameters. A game parameter may includeone or more of an entity class, an entity faction, a usage amount, oneor more usage times, a level, inventory in the virtual space, a score,and/or other game parameters. An activity parameter may include aparameter determined from an activity history of the user in the virtualspace.

The segmentation module may be configured to form segments of the userson the values of one or more of the user parameters. For example, afirst segment of users may be formed based on values of a firstparameter. The first segment may include some portion of the overallusers having values of the first parameter that correspond to eachother. This may include values that are the same, values that are equal,values that fall within a specified range, values that are at least asadjacent as a threshold of some adjacency metric, and/or other valuesthat correspond to each other.

The recommendation generation module may be configured to generate oneor more sets of alternative recommendations for respective one or moresegments of the users. For example, responsive to the one or moresegments of users including a first segment, the recommendationgeneration module may generate at least a first set of alternativerecommendations for the first segment that includes a firstrecommendation and a second recommendation. The first recommendation andthe second recommendation may prompt a user to take a recommended actionin the game in the virtual space. The recommendation generation modulemay be configured such that the first recommendation recommends a firstaction, and the second recommendation recommends a second action that isdifferent from the first action.

The recommendation transmission module may be configured to transmit therecommendations to the appropriate users. The recommendationtransmission module may transmit a recommendation to a user via one ormore of in-game notification, a text message (e.g., SMS, etc.), anemail, a chat message, and instant messenger message, a mobile devicealert, and/or other communication media. The media for transmitting therecommendation may be dictated by the recommendation (e.g. as one of thevariables), based on a user preference or setting, based on adetermination as to whether the user is logged in to the virtual spaceand/or the game, and/or other information. The recommendationtransmission module may be configured to transmit the recommendationresponsive to triggering events.

The recommendation tracking module may be configured to track theresponse of users to the individual recommendations generated by therecommendation generation module. For example, responsive totransmission of the first recommendation to a first subset of users inthe first segment and to transmission of the second recommendation to asecond subset of users in the first segment, the recommendation trackingmodule may track the responses of the first subset of users to the firstrecommendation and may track the responses of the second subset of usersto the second recommendation.

The recommendation analysis module may be configured to assess arelative effectiveness of recommendations transmitted to one or moresegments of users. The effectiveness of the recommendations may bedetermined based on the responses of the one or more segments of users.For example, responsive to the tracking of the responses of the firstsubset of users to the first recommendation and to the tracking of theresponses of the second subset of users to the second recommendation,the recommendation analysis module may determine whether the firstrecommendation or the second recommendation is more effective for thefirst segment of users.

For example, the recommendation analysis module may be configured toassess the relative effectiveness of a given recommendation as afunction of a number of users that perform a recommended action includedin the given recommendation. The recommendation analysis module may beconfigured to assess the relative effectiveness of a givenrecommendation as a function of retention of users that perform arecommended action included in the given recommendation versus usersthat do not perform the recommended action. Retention of users may bequantified, for example, as an average retention time of users thatperform the recommended activity, as a number of users that are retainedfor a threshold amount of time, a number of users that spend a thresholdamount of money in the game, an average revenue per user, and/or otherquantifiers. Time may be time in the game or real-world time. Therecommendation analysis module may also be configured to assess therelative effectiveness of a given recommendation as a function of anaverage amount of participation by users in the game within a givenamount of time after transmission of the recommendation. In anotherexample, the recommendation analysis module may also be configured toassess the relative effectiveness of a given recommendation as afunction of an average amount of time from the transmission of therecommendation to participation by users in the game.

The recommendation generation module may be further configured toreplace recommendations determined by the recommendation analysis moduleto be relatively ineffective for the individual segments. This mayinclude, for example, responsive to a determination that the firstrecommendation is more effective for the first segment than the secondrecommendation for the first segment, generating a third recommendationto be provided as an alternative to the first recommendation for thefirst segment. Subsequent to determination of the third recommendation,the recommendation transmission module may transmit the thirdrecommendation to the first segment of users as an alternative to thefirst recommendation. Transmission of the second recommendation to thefirst segment of users as an alternative to the first recommendation maycease.

These and other objects, features, and characteristics of the systemand/or method disclosed herein, as well as the methods of operation andfunctions of the related elements of structure and the combination ofparts and economies of manufacture, will become more apparent uponconsideration of the following description and the appended claims withreference to the accompanying drawings, all of which form a part of thisspecification, wherein like reference numerals designate correspondingparts in the various figures. It is to be expressly understood, however,that the drawings are for the purpose of illustration and descriptiononly and are not intended as a definition of the limits of theinvention. As used in the specification and in the claims, the singularform of “a”, “an”, and “the” include plural referents unless the contextclearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured to provide targetedrecommendations to segments of users of a virtual space.

FIG. 2 illustrates a view of a graphical user interface configured toreceive entry and/or selection of a definition of a user segment.

FIG. 3 illustrates a method of providing targeted recommendations tosegments of users of a virtual space.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 10 configured to provide targetedrecommendations to segments of users in a virtual space. System 10 maybe configured to provide the virtual space to the users over a network.Providing the virtual space may include hosting the virtual space over anetwork. System 10 may be configured to perform NB testing on users inorder to determine targeted recommendations for segments of users. Atargeted recommendation may enhance a user's enjoyment of the virtualspace and/or increase the chances of success for the user in the virtualspace. The NB testing may be performed on specific segments of theusers, where such segments are determined on user parameters. The userparameters used to segment the users may include one or more ofdemographic parameters, social parameters, game parameters, activityparameters, and/or other parameters. In some implementations, system 10may include a server 12. The server 12 may be configured to communicatewith one or more client computing platforms 14 according to aclient/server architecture. The users may access system 10 and/or thevirtual space via client computing platforms 14.

Server 12 may be configured to execute one or more computer programmodules. The computer program modules may include one or more of a spacemodule 16, a user module 18, a parameter module 20, a segmentationmodule 22, a recommendation generation module 24, a transmission module26, a recommendation redemption module 28, a tracking module 30, ananalysis module 32, and/or other modules.

Space module 16 may be configured to implement the instance of thevirtual space executed by the computer modules to determine viewinformation defining views of the virtual space. The view informationmay then be communicated (e.g., via streaming, via object/position data,and/or other information) from server 12 to client computing platforms14 for presentation to users. The view information determined andtransmitted to a given client computing platform 14 may correspond to anentity being controlled by a user via the given client computingplatform 14. The view information determined and transmitted to a givenclient computing platform 14 may correspond to a location in the virtualspace (e.g., the location from which the view is taken, the location theview depicts, and/or other locations), a zoom ratio, a dimensionality ofobjects, a point-of-view, and/or view parameters. One or more of theview parameters may be selectable by the user.

The instance of the virtual space may comprise a simulated space that isaccessible by users via clients (e.g., client computing platforms 14)that present the views of the virtual space to a user. The simulatedspace may have a topography, express ongoing real-time interaction byone or more users, and/or include one or more objects positioned withinthe topography that are capable of locomotion within the topography. Insome instances, the topography may be a 2-dimensional topography. Inother instances, the topography may be a 3-dimensional topography. Thetopography may include dimensions of the space, and/or surface featuresof a surface or objects that are “native” to the space. In someinstances, the topography may describe a surface (e.g., a groundsurface) that runs through at least a substantial portion of the space.In some instances, the topography may describe a volume with one or morebodies positioned therein (e.g., a simulation of gravity-deprived spacewith one or more celestial bodies positioned therein). The instanceexecuted by the computer modules may be synchronous, asynchronous,and/or semi-synchronous.

The above description of the manner in which views of the virtual spaceare determined by space module 16 is not intended to be limiting. Spacemodule 16 may be configured to express the virtual space in a morelimited, or more rich, manner. For example, views determined for thevirtual space may be selected from a limited set of graphics depictingan event in a given place within the virtual space. The views mayinclude additional content (e.g., text, audio, pre-stored video content,and/or other content) that describes particulars of the current state ofthe place, beyond the relatively generic graphics. For example, a viewmay include a generic battle graphic with a textual description of theopponents to be confronted. Other expressions of individual placeswithin the virtual space are contemplated.

Within the instance(s) of the virtual space executed by space module 16,users may control entities to interact with the virtual space and/oreach other. The entities may include one or more of characters, objects,simulated physical phenomena (e.g., wind, rain, earthquakes, and/orother phenomena), and/or other elements within the virtual space.User-controlled characters may include avatars. As used herein, anentity may refer to an object (or group of objects) present in thevirtual space that represents an individual user. The entity may becontrolled by the user with which it is associated. The user controlledelement(s) may move through and interact with the virtual space (e.g.,non-user characters in the virtual space, other objects in the virtualspace). The user controlled elements controlled by and/or associatedwith a given user may be created and/or customized by the given user.The user may have an “inventory” of virtual goods and/or currency thatthe user can use (e.g., by manipulation of a user character or otheruser controlled element, and/or other items) within the virtual space.

Control over the entities may be exercised by the users through controlinputs and/or commands input through client computing platforms 14. Theusers may interact with each other through communications exchangedwithin the virtual space. Such communications may include one or more oftextual chat, instant messages, private messages, voice communications,and/or other communications. Communications may be received and enteredby the users via their respective client computing platforms 14.Communications may be routed to and from the appropriate users throughserver 12 (e.g., through space module 16).

Within the virtual space, users may participate in a game. The game mayinclude various tasks, levels, quests, and/or other challenges oractivities for users to participate in. The game may include activitiesin which users (or their entities) are adversaries, and/or activities inwhich users (or their entities) are allies. The game may includeactivities in which users (or their entities) are adversaries ofnon-player characters, and/or activities in which users (or theirentities) are allies of non-player characters. In the game, entitiescontrolled by the user may obtain points, virtual currency or othervirtual items, experience points, levels, and/or other demarcationsindicating experience and/or success. Space module 16 may be configuredto perform the functions associated with the game in executing theinstance of the virtual space.

User module 18 may be configured to access and/or manage one or moreuser accounts associated with users of system 10. The user accounts mayinclude user information. The one or more user accounts and/or userinformation may include information stored by server 12, one or more ofthe client computing platforms 14, and/or other storage locations. Theuser accounts may include, for example, information identifying users(e.g., a username or handle, a number, an identifier, and/or otheridentifying information) within the virtual space, security logininformation (e.g., a login code or password), virtual space accountinformation, subscription information, virtual currency accountinformation (e.g., related to currency held in credit for a user),relationship information (e.g., information related to relationshipsbetween users in the virtual space), virtual space usage information,demographic information associated with users, interaction history amongusers in the virtual space, information stated by users, activityinformation of users, browsing history of users, a client computingplatform identification associated with a user, a phone numberassociated with a user, user settings, and/or other information relatedto users. The user information may include and/or indicate an activitylevel of the user. The activity level may include previous logintime(s), previous logout time(s), login frequency, time spent logged in,and/or other activity information. The user information may also includeinformation related to purchases in or for the virtual space. Suchinformation may include, for example, activity information forindividual transactions, a spend rate, a total spend amount, and/orother information related to user purchases.

As will be discussed further below, users may participate in the virtualspace by controlling entities within the virtual space. The userinformation in the user accounts may include information related to theentities controlled by the users in the virtual space. Such informationmay include, for example, an entity type, an entity class, an entityidentification, a level, inventory information, status information,and/or other information related to entities controlled by users in thevirtual space.

Parameter module 20 may be configured to obtain values for one or moreuser parameters. The values may be stored to the user profiles managedby user module 18. The user parameters may include one or more of ademographic parameter, a social parameter, a game parameter, an activityparameter, and/or other parameters.

A demographic parameter may include a parameter related to the realworld demographics of the users. For example, a demographic parametermay include one or more of age, gender, geographic location, language,income, education level, career, marital status, and/or otherdemographic parameters. Parameter module 20 may be configured to obtainvalues of demographic parameters based on registration informationprovided by the user to system 10 (e.g., upon opening an account toparticipate in the virtual space and/or the game), information receivedfrom an online platform from which the virtual space is accessed (e.g.,via a social network website, a microblogging service, and/or otheronline platforms), information derived or deduced from one or more otheruser parameters (e.g., determined from a usage parameter, from a socialparameter, and/or other parameter), and/or from other information orsources.

A social parameter may include a parameter related to a social networkand/or the manner and/or individuals with which a user socializes inand/or out of the virtual space. By way of non-limiting example, asocial parameter may include one or more of a parameter derived from asocial graph in a social network service, an in-game relationship, asocial platform from which the virtual space is accessed, and/or othersocial parameters. Parameter module 20 may be configured to obtainvalues of social parameters from relationships established within thevirtual space (e.g., friendships, alliances, and/or otherrelationships), information received from an online platform from whichthe virtual space is accessed, information related to the usage of thevirtual space (e.g., other users with whom a first user participates inthe virtual space and/or the game, other users with whom a first usercommunicates in the virtual space, and/or other information related tousage), and/or from other information or sources.

A game parameter may include a parameter related to participation and/orusage of the user in the game and/or the virtual space. By way ofnon-limiting example, a game parameter may include one or more of anentity class, an entity faction, a usage amount, one or more usagetimes, a level, inventory in the virtual space, a score, and/or otherparameters. Parameter module 20 may be configured to obtain values ofthe game parameters by monitoring user interaction with the virtualspace. This may include monitoring interactions of users and/or theentities they control in the instance of the virtual space executedspace module 16, and/or monitoring other interactions.

An activity parameter may be related to an activity history of the userin the virtual space. By way of non-limiting example, an activityhistory may include one or more of activity level of the user, virtualcurrency account information of the user, relationship information ofthe user, virtual space usage information of the user, interactionhistory of the user, browsing history of the user, purchase historyand/or other activity history. An activity parameter may also includeone or more of an average participation time in a session of the user,an average time in a session after the user experiences an event, anaverage time between sessions of the game, a value metric representingthe value of the user, a spend velocity, and/or other activityparameters. An event may include, for example, joining an alliance,losing status, losing money, getting lost in the virtual game, losing afriendship, gaining a friendship, losing an alliance, receiving an offerfor a virtual item, purchasing a virtual item, changing a user entity,changing a view of the game, engaging in activity with another user,entering a new level of the game, engaging in a training session,engaging in an in-game experience separate from the game, and/or otherevents. A purchase history may include information relating to, forexample, user purchases, user sales, user exchanges, user browsing forpotential virtual items for purchase, and/or other purchase events. Apurchase may include a transaction in which real world currency isexchanged for one or more virtual items, a transaction in which one ormore virtual items (e.g., virtual currency) is exchanged for another oneor more virtual items, and/or other transactions in which one or morevirtual items are received or given by a user.

Segmentation module 22 may be configured to form segments of the userson one or more of the parameters for which values are obtained byparameter module 20. For example, a first segment of users may be formedbased on values of a first parameter. The first segment may include someportion of the overall users having values of the first parameter thatcorrespond to each other. This may include values that are the same,values that are equal, values that fall within a specified range, valuesthat are at least as adjacent as a threshold of some adjacency metric,and/or other values that correspond to each other. The use of anexemplary segment formed based on a single parameter is not intended tobe limiting. The scope of this disclosure extends to implementations inwhich the first segment is formed on the first parameter, and on one ormore other parameters.

In some implementations, segmentation module 22 may be configured toidentify segments of users that have similar tendencies. For example, asegment of users may have similar activity tendencies, similar usagetendencies, similar responses to recommendations, and/or othertendencies in common. Segmentation module 22 may identify segments ofusers having similar tendencies through analysis of the parametersobtained by parameter module 20, and/or from other sources.

In some implementations, segmentation module 22 may be configured toreceive administrator selection of one or more values for one or moreparameters. Segmentation module 22 may be configured to define agraphical user interface that is provided to an administrator user toreceive entry and/or selection of such values.

By way of illustration, FIG. 2 depicts a view 40 of a graphical userinterface configured to receive entry and/or selection of a definitionof a segment of users of the virtual space. View 40 may include one ormore of a name field 42, a demographic parameter field 44, a socialparameter field 46, a game parameter field 48, an activity parameterfield 50, and/or other fields. Name field 42 may be configured toreceive entry and/or selection of a name for a user segment beingdefined by the administrator. Demographic parameter field 44 may beconfigured to receive entry and/or selection of one or more demographicparameters that should be used to form the user segment. Throughdemographic parameter field 44, the administrator may enter and/orselect parameter value(s) for the entered and/or selected one or moredemographic parameters. The entered and/or selected parameter value(s)may be implemented to determine whether the individual users should beincluded in the user segment. The entered and/or selected parametervalue(s) may specify a value that users should have to included and/orexcluded from the segment, a range to determine whether users should beincluded and/or excluded, and/or may specify inclusion or exclusion inother ways. Social parameter field 46 may be configured to receive entryand/or selection of one or more social parameters that should be used toform the user segment. Through social parameter field 46, theadministrator may enter and/or select parameter value(s) for the enteredand/or selected one or more social parameters. Game parameter field 48may be configured to receive entry and/or selection of one or more gameparameters that should be used to form the user segment. Through gameparameter field 48, the administrator may enter and/or select parametervalue(s) for the entered and/or selected one or more game parameters.Activity parameter field 50 may be configured to receive entry and/orselection of one or more activity parameters that should be used to formthe user segment. Through activity parameter field 50, the administratormay enter and/or select parameter value(s) for the entered and/orselected one or more activity parameters.

Returning to FIG. 1 , recommendation generation module 24 may beconfigured to generate one or more sets of alternative recommendationsfor respective one or more segments of the users. This may include a setof recommendations for the first user segment that includes at least afirst recommendation and a second recommendation. A recommendation mayprompt a user to take one or more recommended actions in the game. Therecommendation generation module 24 may be configured such that thefirst recommendation recommends a first action, and the secondrecommendation recommends a second action that is different from thefirst action. The recommendation generation module 24 may be configuredsuch that the first recommendation recommends a first set of actions,and the second recommendation recommends a second set of actions that isdifferent from the first set of actions. The recommendation generationmodule 24 may also be configured to generate one or more recommendationsfor an individual user based on user parameters. The individual userparameters may be user parameters accessed by segmentation module 22,user parameters associated with the individual user, and/or otherindividual user parameters.

The recommendations within a set of recommendations may differ by one ormore variables. The variables of a recommendation may include one ormore of the action(s) recommended, the types of actions recommended, theformat in which the recommendation is arranged, the method oftransmission, one or more incentives related to the one or morerecommended actions, and/or other variables. A type of action mayinclude action related to a user entity, to a user relationship, to aview, to a user's activity level, to a user's purchasing ability, to avirtual item, and/or other type of action. An incentive to undertake oneor more of the recommended action(s) may include a discount on a priceof a virtual item, an ability to undertake another recommended action,and/or other incentives. Non-limiting examples of recommended actionsmay include, among other things, an upgrade to be obtained for a virtualbuilding, virtual object, virtual troop, or other virtual entities; amembership in a group to be sought (e.g., membership in an alliance,guild, or other group); a virtual building, virtual object, virtualtroop, or other virtual entity to be obtained; a quest or other task tobe undertaken; a battle or conflict to be engaged in; and/or otheractions.

A recommended action may provide a functional advantage in the virtualspace (e.g., an advantage in the game). A recommended action may beexpressed aesthetically within views of the instance of the virtualspace. A recommended action may have value in the virtual space thatfacilitates increasing a bargaining position of the user. For example,an ability to undertake the recommended action may be transferred by theuser to another user for some other consideration within the virtualspace (e.g., for an ability to undertake another action, for arelationship, for an alliance, for a virtual item, for a service, for anadvantage, and/or other consideration). The transfer may involve, forexample, a sale, a sharing, a gift, and/or other transfer. Considerationmay also include, for example, virtual items, virtual currency,agreement to join in an alliance, establishment of a relationship,ceding of territory, agreeing to a treaty and/or peace terms in abattle, increased status in the game, increased power, sharing ofresources, and/or other consideration.

Recommendation generation module 24 may be configured to vary thevariables in between recommendations in the sets of recommendations toperform NB testing on the segments of users. The determination of thevalues of the variables in the individual recommendations (e.g., one ormore of the variables to be varied between the recommendations) may beperformed manually (e.g., through a graphical user interface defined byrecommendation generation module 24) by one or more administrators. Thedetermination of the values of the variables in the individualrecommendations (e.g., one or more of the variables to be varied betweenthe recommendation) may be made automatically. Any technique fordetermining differences between recommendations as part of NB testingmay be implemented without departing from the scope of this disclosure.Recommendation generation module 24 may also be configured to vary thevariables in between recommendations in the sets of recommendations toan individual user based on user parameters for the individual user,user information for the individual user, and/or other user data.

Transmission module 26 may be configured to transmit the recommendationsto the users. Transmission module 26 may transmit a recommendation to auser via one or more of in-game notification, a text message (e.g., viaSMS and/or other text message types), an email, a chat message, andinstant messenger message, a mobile device alert, and/or othercommunication media. The media for transmitting the recommendation maybe dictated by the recommendation (e.g. as one of the variables), basedon a user preference or setting, based on a determination as to whetherthe user is logged in to the virtual space and/or the game, and/or otherinformation.

Transmission module 26 may be configured to transmit the recommendationsresponsive to corresponding triggering events. Such events may include,for example, a game event, a specified real world timing, a userparticipation in the game, a lack of user participation in the game,and/or other events. A trigger for a triggering event may include, forexample, a decrease in an amount of time that the user has spent in asession, an amount of time the user has spent without logging into thegame, a dropoff in a ratio of time playing the game in a single sessionversus time to return to the game in the virtual space, a decrease inparticipation while in the game, a decrease in participation with otherusers, an unexpected ending of a game session by a user, a selling of anunexpected amount user's virtual assets, a breaking and/or weakening ofan alliance, a rejection of a request for a relationship (e.g.,friendship, alliance, and/or other relationship in the game) and/orother triggers. The trigger may be spurred by statistically significantchange or may rely on exceeding a threshold value. The threshold amountmay be, for example, based on an average of all user activity, based onaverage activity of an individual user, set by an administrator, and/orotherwise determined.

Recommendation redemption module 28 may be configured to redeemrecommendations transmitted by recommendation transmission module 26.This may include undertaking a recommended action, treating therecommendation like a virtual item, and/or other redemptions. Forexample, recommendation redemption module 28 may facilitate the sharingor transfer, by the user, of the recommendation. As mentioned above, arecommended action may have value in the virtual space that facilitatesincreasing a bargaining position of the user.

Tracking module 30 may be configured to track the responses of users tothe individual recommendations generated by recommendation generationmodule 24. This may include determining a time spent viewing arecommendation, determining whether a user viewed a recommendation,determining whether a user redeemed a recommendation (e.g., undertakingthe recommended action, transferring the recommendation, and/or otherredemption activity), and/or other tracking activities. By way ofexample, responsive to transmission of the first recommendation to afirst subset of users in the first segment, and to transmission of thesecond recommendation to a second subset of users in the first segment,tracking module 30 may track the responses of the first subset of usersto the first recommendation and/or may track the responses of the secondsubset of users to the second recommendation.

Analysis module 32 may be configured to assess the relativeeffectiveness of recommendations transmitted to the segments of usersbased on the responses of the users. This may include determining, forindividual sets of recommendations, a most effective recommendation in aset of recommendations, a least effective recommendation in a set ofrecommendations, a relative ranking of recommendations in a set ofrecommendations, and/or other determinations related to the relativeeffectiveness of the recommendations in an individual set ofrecommendations. Such determinations may be made based on the responsesof the users as tracked by tracking module 30. The determination thatthe first recommendation is more effective than the secondrecommendation may be made responsive to some threshold number oftransmissions of the first recommendation and/or the secondrecommendation (alone or combined) being made, a level of difference ineffectiveness between the first recommendation and the secondrecommendation breaching a threshold, a level of effectiveness of thesecond recommendation falling below a threshold level, and/or otherevents.

Analysis module 32 may be configured to determine whether the firstrecommendation or the second recommendation is more effective for thefirst segment of users based on the responses of the users in the firstsegment to the first recommendation and the second recommendation. Forexample, analysis module 32 may be configured to assess the relativeeffectiveness of a given recommendation as a function of a number ofusers that perform a recommended action included in the givenrecommendation. Analysis module 34 may also be configured to assess therelative effectiveness of a given recommendation as a function ofretention of users that perform a recommended action included in thegiven recommendation versus users that do not perform the recommendedaction. Retention of users may be quantified, for example, as an averageretention time of users that perform the recommended activity, as anumber of users that are retained for a threshold amount of time, anumber of users that spend a threshold amount of money in the game, anaverage revenue per user, and/or other quantifiers. Analysis module 32may also be configured to assess the relative effectiveness of a givenrecommendation as a function of an average amount of participation byusers in the game within a given amount of time after transmission ofthe recommendation. In another example, analysis module 32 may also beconfigured to assess the relative effectiveness of a givenrecommendation as a function of an average amount of time from thetransmission of the recommendation to participation by users in thegame. Time may be time in the game or real world time.

Recommendation generation module 24 may be configured to replacerecommendations determined by analysis module 32 to be relativelyineffective for the individual segments. For example, responsive to adetermination that the first recommendation is more effective for thefirst segment than the second recommendation for the first segment,recommendation generation module 24 may replace the secondrecommendation with a third recommendation. This may include replacingthe second recommendation with a third recommendation in the first setof recommendations, and/or providing the third recommendation as analternative to the first recommendation for the first segment. The thirdrecommendation may have been previously generated or defined, of may begenerated and/or defined responsive to the determination that the firstrecommendation is more effective for the first segment than the secondrecommendation.

Server 12 and/or client computing platforms 14 may be operatively linkedvia one or more electronic communication links. For example, suchelectronic communication links may be established, at least in part, viaa network such as the Internet and/or other networks. It will beappreciated that this is not intended to be limiting, and that the scopeof this disclosure includes implementations in which server 12 and/orclient computing platforms 14 may be operatively linked via some othercommunication media.

A given client computing platform 14 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable an expert or user associated withthe given client computing platform 14 to interface with server 12,and/or provide other functionality attributed herein to client computingplatforms 14. By way of non-limiting example, the given client computingplatform 14 may include one or more of a desktop computer, a laptopcomputer, a handheld computer, a tablet computing platform, a NetBook, aSmartphone, a gaming console, and/or other computing platforms.

Server 12 may include electronic storage 38, one or more processors 36,and/or other components. Server 12 may include communication lines, orports to enable the exchange of information with a network and/or othercomputing platforms. Illustration of server 12 in FIG. 1 is not intendedto be limiting. Server 12 may include a plurality of hardware, software,and/or firmware components operating together to provide thefunctionality attributed herein to server 12. For example, server 12 maybe implemented by a cloud of computing platforms operating together asserver 12.

Electronic storage 38 may comprise electronic storage media thatelectronically stores information. The electronic storage media ofelectronic storage 38 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server 12and/or removable storage that is removably connectable to server 12 via,for example, a port (e.g., a USB port, a firewire port, etc.) or a drive(e.g., a disk drive, etc.). Electronic storage 38 may include one ormore of optically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage38 may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). Electronic storage 38 may store software algorithms,information determined by processor 36, information received from server12, information received from client computing platforms 14, and/orother information that enables server 12 to function as describedherein.

Processor(s) 36 is configured to provide information processingcapabilities in server 12. As such, processor 36 may include one or moreof a digital processor, an analog processor, a digital circuit designedto process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor 36 is shown in FIG. 1 as asingle entity, this is for illustrative purposes only. In someimplementations, processor 36 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor 36 may represent processing functionality of aplurality of devices operating in coordination. The processor 36 may beconfigured to execute modules 16, 18, 20, 22, 24, 26, 28, 30 and/or 32.Processor 36 may be configured to execute modules 16, 18, 20, 22, 24,26, 28, 30 and/or 32 by software; hardware; firmware; some combinationof software, hardware, and/or firmware; and/or other mechanisms forconfiguring processing capabilities on processor 36.

It should be appreciated that although modules 16, 18, 20, 22, 24, 26,28, 30 and/or 32 are illustrated in FIG. 1 as being co-located within asingle processing unit, in implementations in which processor 36includes multiple processing units, one or more of modules 16, 18, 20,22, 24, 26, 28, 30 and/or 32 may be located remotely from the othermodules. The description of the functionality provided by the differentmodules 16, 18, 20, 22, 24, 26, 28, 30 and/or 32 described below is forillustrative purposes, and is not intended to be limiting, as any ofmodules 16, 18, 20, 22, 24, 26, 28, 30 and/or 32 may provide more orless functionality than is described. For example, one or more ofmodules 16, 18, 20, 22, 24, 26, 28, 30 and/or 32 may be eliminated, andsome or all of its functionality may be provided by other ones ofmodules 16, 18, 20, 22, 24, 26, 28, 30 and/or 32. As another example,processor 36 may be configured to execute one or more additional modulesthat may perform some or all of the functionality attributed below toone of modules 16, 18, 20, 22, 24, 26, 28, 30 and/or 32.

FIG. 3 illustrates a method 50 of hosting a virtual space to clientcomputing devices for interaction by users. The operations of method 50presented below are intended to be illustrative. In some embodiments,method 50 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 50 areillustrated in FIG. 3 and described below is not intended to belimiting.

In some embodiments, method 50 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 50 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 50.

At an operation 52, an instance of a virtual space may be executed. Insome implementations, operation 52 may be performed by a space modulethe same as or similar to space module 16 (shown in FIG. 1 and describedabove).

At an operation 54, the executed instance of the virtual space may beimplemented to determine view information. The view information maydefine views to be presented to users via client computing platforms. Insome implementations, operation 54 may be performed by a space modulethe same as or similar to space module 16 (shown in FIG. 1 and describedabove).

At an operation 56, values of user parameters for users of the virtualspace may be obtained. In some implementations, operation 56 may beperformed by a parameter module the same as or similar to parametermodule 20 (shown in FIG. 1 and described above).

At an operation 58, a first segment of users may be formed on the valuesof one or more of the parameters as obtained at operation 56. The firstsegment of users may be formed on one or more values of the firstparameter. In some implementations, operation 58 may be performed by asegmentation module the same as or similar to segmentation module 22(shown in FIG. 1 and described above).

At an operation 60, a set of recommendations for the first segment ofusers may be determined. The set of recommendations may include a firstrecommendation and a second recommendation. The first recommendation mayinclude a first action different from a second action included in thesecond recommendation. The first recommendation may different from thesecond recommendation by one or more variables. In some implementations,operation 60 may be performed by a recommendation generation module thesame as or similar to recommendation generation module 24 (shown in FIG.1 and described above).

At an operation 62, the recommendations in the one or more sets ofrecommendations may be transmitted to the users in the first segment.This may include transmitting the first recommendation to a first subsetof the users in the first segment, and transmitting the secondrecommendation to a second subset of the users in the first segment. Insome implementations, operation 62 may be performed by a transmissionmodule the same as or similar to transmission module 26 (shown in FIG. 1and described above).

At an operation 64, responses of the users in the first segment to therecommendations in the one or more sets of recommendations may betracked. In some implementations, operation 64 may be performed by atracking module the same as or similar to tracking module 30 (shown inFIG. 1 and described above).

At an operation 66, the effectiveness of the recommendations in the oneor more sets of recommendations for the first segment may be determined.This may include determining the relative effectiveness for the firstsegment of the first recommendation, the second recommendation, and/orother recommendations in the one or more sets of recommendations. Insome implementations, operation 66 may be performed by an analysismodule the same as or similar to analysis module 32 (shown in FIG. 1 anddescribed above).

For example, analysis module 32 may be configured to assess the relativeeffectiveness of a given recommendation as a function of a number ofusers that perform a recommended action included in the givenrecommendation. Analysis module 32 may also be configured to assess therelative effectiveness of a given recommendation as a function ofretention of users that perform a recommended action included in thegiven recommendation versus users that do not perform the recommendedaction. Retention of users may be quantified, for example, as an averageretention time of users that perform the recommended activity, as anumber of users that are retained for a threshold amount of time, anumber of users that spend a threshold amount of money in the game, anaverage revenue per user, and/or other quantifiers. Analysis module 32may also be configured to assess the relative effectiveness of a givenrecommendation as a function of an average amount of participation byusers in the game within a given amount of time after transmission ofthe recommendation. In another example, analysis module 32 may also beconfigured to assess the relative effectiveness of a givenrecommendation as a function of an average amount of time from thetransmission of the recommendation to participation by users in thegame.

Although the system(s) and/or method(s) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation can be combined with one or morefeatures of any other implementation.

What is claimed is:
 1. A system configured to provide differentrecommendations to users of a game and assess effectiveness of thedifferent recommendations, the system comprising: one or more processorsconfigured by machine-readable instructions to: execute an instance ofthe game, and to implement the instance of the game to facilitateparticipation by the users in the game by receiving control inputsentered by the users, wherein the control inputs facilitate interactionbetween the users and the game; generate automatically, by the one ormore processors, one or more recommendations for one or more segments ofthe users including a first segment of the users, wherein arecommendation prompts a particular user to take a particularrecommended action in the game, wherein the recommendations include afirst recommendation recommending a first recommended action and asecond recommendation recommending a second recommended action that isdifferent from the first recommended action; effectuate delivery of thefirst and second recommendations to different users in the first segmentof the users, such that the first recommendation is delivered to a firstsubset of users in the first segment and the second recommendation isdelivered to a second subset of users in the first segment; trackresponses of the users of the first segment to the first and secondrecommendations by (i) making a first determination of a firsteffectiveness of the first recommendation, and (ii) making a seconddetermination of a second effectiveness of the second recommendation;generate a new recommendation for a particular subset of the users,wherein the new recommendation is based on one or more of the firstrecommendation, the second recommendation, the first determination,and/or the second determination; and effectuate delivery of the newrecommendation to the particular subset of the users.
 2. The system ofclaim 1, wherein the responses of the users are further tracked by:(iii) determining whether individual users in the first subset of usershave effectuated performance of the first recommended action, (iv)determining whether individual users in the second subset if users haveeffectuated performance of the second recommended action, wherein makingthe first determination is further based on the first effectiveness forthose users in the first subset of users who performed the firstrecommended action, and wherein making the second determination isfurther based on the second effectiveness for those users in the secondsubset of users who performed the second recommended action.
 3. Thesystem of claim 2, wherein the one or more processors are furtherconfigured by machine-readable instructions to determine a relativeeffectiveness of a particular recommendation as a function of a numberof users that perform a recommended action included in the particularrecommendation.
 4. The system of claim 2, wherein the one or moreprocessors are configured by machine-readable instructions to determinea relative effectiveness of a particular recommendation for a particularrecommended action as a function of users that perform the particularrecommended action of the particular recommendation versus users that donot perform the particular recommended action of the particularrecommendation.
 5. The system of claim 2, where the one or moreprocessors are configured by machine-readable instructions to determinea relative effectiveness of the first recommendation with respect to thesecond recommendation by comparing the first effectiveness and thesecond effectiveness.
 6. The system of claim 5, wherein the firsteffectiveness is quantified as one or more of an average retention timeof users in the first subset of users, an average retention time ofusers in the first subset that effectuated performance of the firstrecommended action, a number of users in the first subset that areretained for a threshold amount of time, a number of users in the firstsubset that spend a threshold amount of money on the game, and/or anaverage revenue per user in the first subset.
 7. The system of claim 1,wherein the one or more processors are further configured bymachine-readable instructions to form the one or more segments of theusers based on one or more of a demographic parameter, a socialparameter, a game parameter, or an activity parameter.
 8. The system ofclaim 7, wherein the one or more processors are configured bymachine-readable instructions to form the one or more segments of theusers based on a demographic parameter, and wherein the demographicparameter includes one or more of age, sex, geographic location,language, income, education, career, or marital status.
 9. The system ofclaim 7, wherein the one or more processors are configured bymachine-readable instructions to form the one or more segments of theusers based on at least a social parameter, and wherein the socialparameter includes one or more of a parameter derived from a socialgraph in a social network service, an in-game relationship, or aplatform from which the game is accessed.
 10. The system of claim 7,wherein the one or more processors are configured by machine-readableinstructions to form the one or more segments of the users based on atleast a game parameter, and wherein the game parameter includes one ormore of an entity class, an entity faction, a usage amount, one or moreusage times, a level, inventory in the game, or a score.
 11. The systemof claim 7, wherein the one or more processors are configured bymachine-readable instructions to form the one or more segments of theusers based on at least an activity parameter, and wherein the activityparameter includes a parameter determined from an action history of theuser in the game.
 12. The system of claim 1, wherein the one or moreprocessors are configured by machine-readable instructions to determinea relative effectiveness of the first recommendation with respect to thesecond recommendation as a function of an average amount ofparticipation by the first subset of users in the game within a giventime after transmission of the first recommendation relative to anaverage amount of participation by the second subset of users in thegame within the given time after transmission of the secondrecommendation.
 13. The system of claim 1, wherein the one or moreprocessors are configured by machine-readable instructions to determinea relative effectiveness of the first recommendation with respect to thesecond recommendation as a function of an average amount of time fromthe transmission of the first recommendation to the first subset toparticipation by the first subset of users in the game relative to anaverage amount of time from the transmission of the secondrecommendation to the second subset to participation by the secondsubset of users.
 14. A computer-implemented method of providingdifferent recommendations to users of a game and assessing effectivenessof the different recommendations, the method being implemented in acomputer system comprising one or more physical processors, the methodcomprising: executing an instance of the game; implementing the instanceof the game to facilitate participation by the users in the game byreceiving control inputs entered by the users, wherein the controlinputs facilitate interaction between the users and the game; generatingautomatically, by the one or more physical processors, a firstrecommendation and a second recommendation for a first segment of theusers, wherein a recommendation prompts a particular user to take aparticular recommended action in the game, wherein the firstrecommendation recommends a first recommended action, and wherein thesecond recommendation recommends a second recommended action that isdifferent from the first recommended action; effectuating delivery ofthe first and second recommendations to different users in the firstsegment of the users, such that the first recommendation is delivered toa first subset of users in the first segment and the secondrecommendation is delivered to a second subset of users in the firstsegment; tracking responses of the users of the first segment to thefirst and second recommendations by (i) making a first determination ofa first effectiveness of the first recommendation, and (ii) making asecond determination of a second effectiveness of the secondrecommendation; generating a new recommendation for a particular subsetof the users, wherein the new recommendation is based on one or more ofthe first recommendation, the second recommendation, the firstdetermination, and/or the second determination; and effectuatingdelivery of the new recommendation to the particular subset of theusers.
 15. The method of claim 14, wherein the responses of the usersare further tracked by: (iii) determining whether individual users inthe first subset of users have effectuated performance of the firstrecommended action, (iv) determining whether individual users in thesecond subset if users have effectuated performance of the secondrecommended action, wherein making the first determination is furtherbased on the first effectiveness for those users in the first subset ofusers who performed the first recommended action, and wherein making thesecond determination is further based on the second effectiveness forthose users in the second subset of users who performed the secondrecommended action.
 16. The method of claim 14, wherein the firstsegment of the users is based on a first parameter, and wherein thefirst parameter includes one or more of a demographic parameter, asocial parameter, a game parameter, and/or an activity parameter. 17.The method of claim 16, wherein the first parameter includes at least ademographic parameter, and wherein the demographic parameter includesone or more of age, sex, geographic location, language, income,education, career, or marital status.
 18. The method of claim 16,wherein the first parameter includes at least an activity parameter, andwherein the activity parameter includes a parameter determined from anaction history of the user in the game.
 19. The method of claim 14,further comprising determining a relative effectiveness of a particularrecommendation as a function of the number of users that perform arecommended action included in the particular recommendation.
 20. Themethod of claim 14, further comprising determining a relativeeffectiveness of a particular recommendation as a function of users thatperform a recommended action included in the particular recommendationversus users that do not perform the recommended action included in theparticular recommendation.